The WHY of Data Glossary in Data Governance Adoption
I've come to realize that when people don't understand the 'WHY' of an activity, they can never appreciate the value and it would be absolutely impossible to realize the intended purpose. This same notion goes for Data Governance adoption. If an organization does not truly understand the WHY and reason behind key data governance activities, they'll never truly be able to harness the full potential of the value-add to their enterprise.
The last few weeks, I've been intentional in sharing some of the 'Whys' in Data Governance activities. Still on the plight to unravel the fog undermining the value-add of this discipline and why selling it prove to be a struggle in so many organizations. The word 'Data Governance' is no longer a strange phrase to many as they've heard this one way or the other in so many conversations and in a lot of organizations that have kicked off their Data Governance adoption for years without traction. The sad truth is that many cannot truly articulate why they embark on this journey and why they engage certain activities in their adoption. This is simply because the narratives over the years has been really poor.
I've engaged with many in my journey over the years through this discipline. Many organizations have raised poster of Data Governance initiative in their organizations. Only for you to find out it's nothing close to Governing Data. Some will paint a cleansing, fixing project as a Data Governance initiative. Others will cherry pick some of the activities in governance the minute they kickoff Data Governance and work tirelessly at it simply because they've seen others doing the same. They run with the 'sound-bites' of what activities without truly understanding the 'why'.
We are at a point where I believe practitioners need to be intentional about calling out the 'why' as we open up the conversation around Data Governance activities. We simply need to throw out all the meaningless definitions that over-complicate what this discipline is all about. Let's boil it down to the why we govern, what we govern and how we govern.
I believe it's easier for our audience to understand when we focus on the purpose and intent of Data Governance. I believe it serves us well to explain why we engage our different activities and standards of care for governance with clarity. I believe such narratives will be easier for our audience to understand as we walk them through how such activities will help get them to their desired goals and strategic vision of their organization.
To this effect, I believe its important for us to call out how adoption of Data Governance help put in place several pillars of realizing needed cultural transformation and oversight of the key controls to enable organization to manage, optimize, protect and leverage their data. Thereby instilling sustainable ethics of engagement and standards of care to their data asset.
So, this week. We'll be focusing on one of those Standards of Care – Building A Data Glossary(Business Metadata).
Why do we build Data Glossaries and why is this important as a Data Governance activity?
Organizations have been creating and using data for decades to drive their business as much as possible. We have been engaging and making decisions from data for decades. Yet, we haven't fully been able to harness the full potential in our data simply because our data is often in poor state as the trustworthiness of data in most organizations are questionable.
One of the main reasons the quality of our data is compromised is very fundamental as there are lots of ambiguities in our data. This often leaves consumers with a lot of guesses and assumptions that result in the misuse of data as everyone use the same data differently. This is because we have not done a great job defining the purpose and guardrails of usage for our data; we've left room for consumers to insert their own interpretation of the intended purpose of the data they engaged as a result of lack of meaningful and standardized definition around the rule of engaging our data.
The reality of this gap over the years is that it has cost so many organizations a lot of money; a lot of poor decisions have been made and many have paid heavy penalties for the misuse and missed business opportunities due to the same.
Now, this is exactly where Data Glossary come to play. This is the hole Data Glossary is positioned to solve for in our Data Governance adoption.
What is a Data Glossary?
A data glossary (either in an automated tool or manual) is a repository of key terms that bring together an acceptable common definition of Data attributes and terms across the enterprise. Not to be confused with Data Dictionaries, but work hand in hand with Data Dictionaries.
A Data dictionary typically provides our technical audience the blueprint of the enterprise data elements within the technology it sits. A data dictionary attempts to provide our technical audience some guidance around the intended structure of the data. i.e. size, format, data types, etc.
A Business Glossary or Data Glossary, however, is an enriched version of the enterprise data elements that provide more meaning and richer metadata that speaks to the business consumers' understanding of the intent and use of that data.
It provides a clear narrative around the usage of the data for anyone to understand. Thereby avoiding the misuse or any ambiguity in its usage.
It provides clarity on who should have access to the data and the level of Sensitivity around the data
It details the key ownership roles and responsibilities appointed on the data as the authoritative voices
It provides guardrails and acceptable value expectations around the data element
It articulates clear rules of engaging the data in form of data quality rules.
What is the WHY of Data Glossary?
Data Glossary attempts to focus on the clarity of metadata. Removing ambiguity for enterprise data creation and consumption. It focuses on preventing the introduction of poor-quality data to our ecosystem by documenting richer, meaningful purpose and rule of engaging a data attribute. It provides the enterprise a standardized engagement inventory and a point of reference for every data consumer to engage before the creation and use of the data. It describes the fine print and guardrails for every data attribute as part of the preventive care against poor ethics and misuse of the same.
Data Glossary aims to fulfill governance requirement to provide a documented single definition for each enterprise data, validated and ratified by all stakeholders as the common taxonomy of use by the enterprise throughout the data value chain.
It aims to provide a single point of definition, nomenclatures for allowable rules, and list of values for each data attribute.
Data Glossary is a key governance deliverable and a reference point for acceptable standards for the enterprise data asset. It is therefore very important that Data Glossary mirrors the outlined purposes as called out above. A data glossary is as good as the richness of its content.
How Do You Create a Rich, Value-Add Business Glossary:
Here are some practical tips:
· It must be comprehensive of the enterprise key data attribute. Identify reports and other materials produced for internal and external stakeholders to ensure it's comprehensiveness
· You must engage any available documentation at the onset of creating a Data Glossary to get some insight i.e. Data Dictionaries.
· Interview subject matter experts in each line of business and domain support areas
· Facilitate discussions among stakeholders to achieve consensus on definition and ownership
· Collaborate input with Data Owners and SMEs across the enterprise.
· Ensure all Data Domain & Business communities are well represented in the input and development of your Data Glossary
· Have a consensus definition of terms and purpose of use for each enterprise attribute.
· Engage industry standards for guidance on universally used data attributes
· Your Data Glossary must be well documented, ratified, published, and accessible to all data citizens to engage in their daily operations.
· Have a defined Change Management over process on updates to your Data Glossary.
In Summary, your data glossary must have the highlighted understanding above in mind at the onset of its creation. You must know the WHY, understand the WHAT and develop it in a collaborative way with every stakeholder's input and agreement of defined content for the enterprise use. The term and definitions of use in a Data Glossary must represent the acceptable common taxonomy of the data across the enterprise. It must aim at achieving unique, meaningful metadata for your business community and the enterprise.
For more detail and help on Practical Data Governance activation for optimal ROI. Book a Free Call with me to discuss your challenges and we can explore simple strategies to actualize your governance success.